The method behind every recommendation.
What is examined, in what order, and what happens when the assumptions change.
The rationale comes before the solution.
Many AI projects start with a solution. Operient starts with the operation: every opportunity is scored against the same criteria, and the result is an evidence-based investment recommendation.
The aim is to establish which opportunities should be funded, in what order and on what basis.
Scope
You choose the process area to assess and which opportunities are in scope.
Current state
How the work is done today is recorded, so value can be measured against your actual starting point.
AI opportunities
Where AI fits the work, and what it must meet in data, oversight and operations.
Is the operation ready?
Whether data, systems, oversight and people can support the change.
Investment prioritisation
Opportunities are ranked on the same measures. Value, cost, risk and capacity to deliver are weighed together, and scenarios show what changes if the assumptions change.
Recommendation and rationale
A clear recommendation on what should be prioritised, why, and when the decision should be reviewed.
Every assumption, result and decision stays on record, so you can trace how the result was reached and review it later.
The first assessment covers one process area. Once more areas are assessed, you can prioritise across them.
The same questions for every opportunity.
The answers separate a good idea from an investment you can defend.
Is there a real problem to solve?
Whether the work today is costly, slow or error-prone, and whether it matters enough to the business.
Where can AI improve the work?
Which parts of the work suit AI, given what the technology can do today, and which stay with people.
What needs to be in place?
Whether the data, systems and people are in place, and which governance requirements apply.
Does the investment pay off?
Expected value and cost, how much uncertainty there is, and which assumptions the result rests on.
Should this happen now?
Whether the opportunity should be funded now, run as a governed pilot, deferred or kept human-led.
You can always show what a recommendation was based on.
Data, assumptions and rationale are recorded with every recommendation. Months later, you can see both what was decided and why.
Recorded with every recommendation
The data, assumptions, capability snapshot version and the decision itself are stored together.
Assumptions change
When the operation, the rules or AI capabilities change, you update the assumptions in Operient.
Updated and compared
A new result is calculated and shown next to the previous one, so it is clear what changed and why.
With more process areas, you see the whole picture.
The first assessment delivers a decision document for one process area. As more are added, every opportunity is ranked on the same basis, and the ranking is updated when circumstances change.
- Invoice exception handling
- Service request triage
- Contract review
- Supplier onboarding
- Claims processing
- Regulatory reporting
- 78 Ticket classification
- 68 Invoice exception handling
- 58 Contract review
- 44 New supplier onboarding
- 39 Claims handling
- 12 Regulatory reporting
- 01Ticket classificationInvest now
- 02Invoice exception handlingGoverned pilot
- 03Contract reviewGoverned pilot
- 04New supplier onboardingDefer
- 05Claims handlingDefer
- 06Regulatory reportingKeep human-led
Six of ten opportunities are shown here. Statuses are revisited when data or assumptions change.
Sample with fictional data. The company is not an Operient client.
Boards and regulators expect more and more from how AI investments are made. Operient supports structured, evidence-based governance of those investments.
Operient does not replace risk classification under the EU AI Act, compliance assessment, or the security and organisational measures companies are required to maintain.
Start with one process area.
In the call, we go through which process area is the best place to start.